Nowadays, Connected Automated Vehicles (CAVs) have emerged as powerful infrastructures for the next-generation Intelligent Transportation System (ITS) as the rapid technological advancements of communication networks and vehicular intelligence. While prospective platoon-based techniques in CAVs, the heterogeneous traffic condition poses a challenge for platoon control in the self-organized traffic bottleneck, thus making an urgent need for a practical sustainable transportation architecture. To address this problem, we propose a software defined architecture that leverages multi-agent techniques to mobile-edge computing networks for multi-vehicle adaptive platoon, which is called SD-M3ASP. The architecture supports centralized and decentralized management of vehicular edge communication resources between mobile vehicles and edge devices, and underpins sustainable vehicular platooning capabilities. Then, we propose cluster-based kinematic models by grouping vehicles into multi-vehicle clusters (MVCs) to facilitate efficient platoon control with collision avoidance. Furthermore, we propose three-stage platoon control algorithms to adaptively balance the size of MVCs and form stable platoons in heterogeneous traffic flows. The intra-platoon and inter-platoon convergence are analyzed by using the Routh stability criterion and Lyapunov technique. A CAV simulation software is developed for demonstration purposes which is available online athttps://qgailab.com/cav-sim. Extensive numerical simulation results have shown the superiority of the proposed method, which can greatly eliminate the self-organized congestion caused by heterogeneous traffic flow.

With the rapid development of wireless communication and localization technologies, the easier collection of trajectory data can bring potential data-driven value. Recently, there has been an increasing interest in how to publish trajectory dataset without revealing personal information. However, since the large-scale and real-world sequential trajectory dataset presents a heterogeneous regional distribution, the existing study ignores the relationship between privacy budget allocation and spatial characteristics, resulting in unreasonable continuity and mapping distortion, and thus lowering the utility of the synthetic dataset. To address this problem, we propose a probability distribution model named Adaptive grid-based Weighted Differential Privacy (AWDP). First, trajectories are adaptively discretized into the multi-resolution grid structures to make trajectories more uniformly distributed and less disturbed by the noise. Second, we allocate different weighted budgets for different grids according to density-based regional characteristics. Third, a spatio-temporal continuity maintenance method is designed to solve unrealistic direction- and density-based continuity deviations of synthetic trajectories. An application system is developed for demonstration purposes which is available online athttp://qgailab.com/awdp/. The extensive experiments on three datasets demonstrate that AWDP performs significantly better than the state-of-the-art model in preserving the density distribution of the original trajectories with differential privacy guarantee and high utility.

Recent developments in multiagent consensus problems have heightened the role of network topology when the agent number increases largely. The existing works assume that the convergence evolution typically proceeds over a peer-to-peer architecture where agents are treated equally and communicate directly with perceived one-hop neighbors, thus resulting in slower convergence speed. In this article, we first extract the backbone network topology to provide a hierarchical organization over the original multiagent system (MAS). Second, we introduce a geometric convergence method based on the constraint set (CS) under periodically extracted switching-backbone topologies. Finally, we derive a fully decentralized framework named hierarchical switching-backbone MAS (HSBMAS) that is designed to conduct agents converge to a common stable equilibrium. Provable connectivity and convergence guarantees of the framework are provided when the initial topology is connected. Extensive simulation results on different-type and varying-density topologies have shown the superiority of the proposed framework.

Trajectory datasets have been widely used in transportation research, but the risk of privacy breach comes with data sharing. Privacy budget allocation is a key step of the differential privacy (DP)-based privacy-preserving data publishing (PPDP) algorithm development, as it directly impacts the data utility of the released dataset. Most prior research used simple logic to allocate privacy budgets, such as evenly distributing them among different tree levels, without theoretical support to reach optimality. This manuscript presents the development of an optimal privacy budget allocation algorithm for transit smart card data, with the goal of publishing non-interactive sanitized trajectory data under a differential privacy definition. To this end, the smart card trajectory data are first stored in a prefix tree structure, and a query probability model is developed to quantitatively measure the probability of a trajectory location pair being queried. Next, the privacy budget is optimized for each prefix tree node to minimize the query error, while satisfying the differential privacy definition. The Lagrangian relaxation method is adopted to derive the optimal privacy budget values, and several propositions on the solution property are proposed and proved. Real-life metro smart card data from Shenzhen, China that include a total of 2.8 million individual travelers and over 220 million records are used in the case study section. The developed algorithm is demonstrated to output a sanitized dataset with the highest utilities when compared with three benchmark algorithms. Sensitivity analysis shows that the resulting data utility remains stable when the privacy budget changes. The runtime of the proposed algorithm is less than 160 seconds in all experiments, exhibiting good computational efficiency.

The city's district attraction ranking plays an essential role in the city's government because it can be used to reveal the city's district attraction and, thus, help government make decisions for urban planning in terms of the smart city. The traditional methods for urban planning mainly rely on the district's GDP, employment rate, population density, information from questionnaire surveys, and so on. However, as a comparison, such information is becoming relatively less informative as the explosion of an increasing amount of urban data. What is more, there is a serious shortcoming in these methods, i.e., they are independent representations of the attraction of a district and do not take into account the interaction among districts. With the development of urban computing, it is possible to make good use of urban data for urban planning. To this end, based on taxi big data obtained from Guangzhou, China, this article proposes a district attraction ranking approach called AttractRank, which for the first time uses taxi big data for district ranking. An application system is developed for demonstration purposes. First, the entire Guangzhou city is divided into a number of districts by using constrained K-means. Second, the original PageRank algorithm is extended to integrate with the taxi's origin-destination (OD) points to establish the OD matrix, whereby the attraction ranking of each district can be calculated. Finally, by visualizing the results and case studies obtained from AttractRank, we can successfully obtain the pattern of how attractions of districts change over time and interesting discoveries on urban lives; therefore, it has wide applications in urban planning and urban data mining.